Hysteresis effects in Bavarian beech forest ecosystems due to climate extremes (HyBBEx)

Forest decline, in course of climate change, has become a frequently observed phenomenon. Much of the observed decline has been associated with an increasing frequency of climate change induced hotter droughts while decline induced by flooding, late-frost, and storms also play an important role. As a consequence, tree mortality rates have increased across the globe. Despite numerous studies that have assessed forest decline and predisposing factors for tree mortality, we still lack an in-depth understanding of (I) underlying eco-physiological mechanisms, (II) the influence of varying environmental conditions related to soil, competition, and micro-climate, and (III) species-specific strategies to cope with prolonged environmental stress. To deepen our knowledge within this context, studying tree performance within larger networks seems a promising research avenue. Ideally such networks are already established during the actual period of environmental stress. One approach for identifying stressed forests suitable for such monitoring networks is to assess measures related to tree vitality in near real-time across large regions by means of satellite-borne remote sensing. Within this context, we introduce the European Forest Condition monitor (EFCM)—a remote-sensing based, freely available, interactive web information tool. The EFCM depicts forest greenness (as approximated using NDVI from MODIS at a spatial resolution of roughly 5.3 hectares) for the pixel-specific growing season across Europe and consequently allows for guiding research within the context of concurrent forest performance. To allow for inter-temporal comparability and account for pixel-specific features, all observations are set in relation to normalized difference vegetation index (NDVI) records over the monitoring period beginning in 2001. The EFCM provides both a quantile-based and a proportion-based product, thereby allowing for both relative and absolute comparison of forest greenness over the observational record. Based on six specific examples related to spring phenology, drought, late-frost, tree die-back on water-logged soils, an ice storm, and windthrow we exemplify how the EFCM may help identifying hotspots of extraordinary forest greenness. We discuss advantages and limitations when monitoring forest condition at large scales on the basis of moderate resolution remote sensing products to guide users toward an appropriate interpretation.

The growth of past, present, and future forests was, is and will be affected by climate variability. This multifaceted relationship has been assessed in several regional studies, but spatially resolved, large-scale analyses are largely missing so far. Here we estimate recent changes in growth of 5800 beech trees (Fagus sylvatica L.) from 324 sites, representing the full geographic and climatic range of species. Future growth trends were predicted considering state-of-the-art climate scenarios. The validated models indicate growth declines across large region of the distribution in recent decades, and project severe future growth declines ranging from −20% to more than −50% by 2090, depending on the region and climate change scenario (i.e. CMIP6 SSP1-2.6 and SSP5-8.5). Forecasted forest productivity losses are most striking towards the southern distribution limit of Fagus sylvatica, in regions where persisting atmospheric high-pressure systems are expected to increase drought severity. The projected 21st century growth changes across Europe indicate serious ecological and economic consequences that require immediate forest adaptation.

The mechanistic pathways connecting ocean-atmosphere variability and terrestrial productivity are well-established theoretically, but remain challenging to quantify empirically. Such quantification will greatly improve the assessment and prediction of changes in terrestrial carbon sequestration in response to dynamically induced climatic extremes. The jet stream latitude (JSL) over the North Atlantic-European domain provides a synthetic and robust physical framework that integrates climate variability not accounted for by atmospheric circulation patterns alone. Surface climate impacts of north-south summer JSL displacements are not uniform across Europe, but rather create a northwestern-southeastern dipole in forest productivity and radial-growth anomalies. Summer JSL variability over the eastern North Atlantic-European domain (5-40E) exerts the strongest impact on European beech, inducing anomalies of up to 30% in modelled gross primary productivity and 50% in radial tree growth. The net effects of JSL movements on terrestrial carbon fluxes depend on forest density, carbon stocks, and productivity imbalances across biogeographic regions.

Tree-ring chronologies encode interannual variability in forest growth rates over long time periods from decades to centuries or even millennia. However, each chronology is a highly localized measurement describing conditions at specific sites where wood samples have been collected. The question whether these local growth variabilites are representative for large geographical regions remains an open issue. To overcome the limitations of interpreting a sparse network of sites, we propose an upscaling approach for annual tree-ring indices that approximate forest growth variability and compute gridded data products that generalize the available information for multiple tree genera. Using regression approaches from machine learning, we predict tree-ring indices in space and time based on climate variables, but considering also species range maps as constraints for the upscaling. We compare various prediction strategies in cross-validation experiments to identify the best performing setup. Our estimated maps of tree-ring indices are the first data products that provide a dense view on forest growth variability at the continental level with 0.5° and 0.0083° spatial resolution covering the years 1902–2013. Furthermore, we find that different genera show very variable spatial patterns of anomalies. We have selected Europe as study region and focused on the six most prominent tree genera, but our approach is very generic and can easily be applied elsewhere. Overall, the study shows perspectives but also limitations for reconstructing spatiotemporal dynamics of complex biological processes. The data products are available at https://www.doi.org/10.17871/BACI.248.

Detecting pointer years in tree-ring data is a central aspect of dendroecology. Pointer years are usually represented by extraordinary secondary tree growth, which is often interpreted as a response to abnormal environmental conditions such as late-frosts or droughts. Objectively identifying pointer years in larger tree-ring networks and relating those to specific climatic conditions will allow for refining our understanding of how trees perform under extreme climate and consequently, under anticipated climate change. Recently, Buras et al. (2020) demonstrated that frequently used pointer-year detection methods were either too sensitive or insensitive for such large scale analyses. In their study, Buras et al. (2020) proposed a novel approach for detecting pointer years – the standardized growth change (SGC) method which outperformed other pointer-year detection methods in pseudopopulation trials. Yet, the authors concluded that SGC could be improved further to account for the inability to detect pointer years following successive growth decline. Under this framework, we here present a refined version of the SGC-method – the bias-adjusted standardized growth change method (BSGC). The methodological adjustment to the SGC approach comprises conflated probabilities derived from standardized growth changes with probabilities derived from a time-step specific global standardization of growth changes. In addition, BSGC allows for estimating the length of the deflection period, i.e. the period before extraordinary growth values have reached normal levels. Application of BSGC to simulated and measured tree-ring data indicated an improved performance in comparison to SGC which allows for the identification of pointer years following years of successive growth decline. Also, deflection period lengths were estimated well and revealed plausible results for an existing tree-ring data set. Based on these validations, BSGC can be considered a further refinement of pointer-year detection, allowing for a more accurate identification and consequently better understanding of the radial growth response of trees to extreme events.